Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. In order to emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make Climate a domain which is rich in interesting machine learning challenges.
Here I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate.
This popular article provides a short summary of the progress and prospects in Weather and Climate Modelling for the benefit of high school and undergraduate college students and early career researchers. Although this is not a comprehensive scientific article, the basic information provided here is intended to introduce students and researchers to the topic of Weather and Climate Modelling - which comes under the broad discipline of Atmospheric / Oceanic / Climate / Earth Sciences. This article briefly summarizes the historical developments, progress, scientific challenges in weather and clim…
Numerical weather prediction has traditionally been based on physical models of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data-driven medium-range weather forecasting with first studies exploring the feasibility of such an approach. To accelerate progress in this area, the WeatherBench benchmark challenge was defined. Here, we train a deep residual convolutional neural network (Resnet) to predict geopotential, temperature and precipitation at 5.625 degree resolution up to 5 days ahead. To avoid overfitting and improve forecast skill, …
Generative assimilation and prediction for weather and climate
Chinese Academy of Sciences（中国科学院）；Nanjing University（南京大学）；Technical University of Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；National University of Defense Technology（国防科技大学）；Peking University（北京大学）；National Climate Center（国家气候中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究院）
Neural General Circulation Models for Weather and Climate
Google Research（谷歌研究院）；Massachusetts Institute of Technology（麻省理工学院）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Google DeepMind（谷歌DeepMind）；Harvard University（哈佛大学）
This paper studies the chaotic behavior of hydrosphere and its influence on global weather and climate. We give mathematical arguments for the sea surface temperature (SST) to be unpredictable over the global ocean. The impact of SST variability on global climate is clear during global climate patterns, which involve large-scale ocean-atmosphere fluctuations similar to the El Niño-Southern Oscillation (ENSO). Sensitivity (unpredictability) is the core ingredient of chaos. Several researches suggested that the ENSO might be chaotic. It was Vallis [1] who revealed unpredictability of ENSO by red…
Increasing trends in the severity of Australian fire weather conditions over the past century
Federation University, Australia（联邦大学）；University of Melbourne, Melbourne, Victoria, Australia（墨尔本大学）；ARC Centre of Excellence for 21st Century Weather, Melbourne, Victoria, Australia（21世纪天气卓越研究中心）
AIMIP Phase 1: systematic evaluations of AI weather and climate models
Allen Institute for Artificial Intelligence（人工智能艾伦研究所）；Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research（阿尔弗雷德·韦格纳极地海洋研究所，赫尔姆霍茨极地海洋研究中心）；European Center for Medium Range Weather Forecasts（欧洲中期天气预报中心）；University of Maryland（马里兰大学）；INRIA（法国国家研究院计算机科学研究所）；Google DeepMind（谷歌DeepMind）；Otto-von-Guericke Universität（奥托·冯·格里克大学）；University of Potsdam（波茨坦大学）；University of Washington（华盛顿大学）；NVIDIA Corporation（英伟达公司）；Google Research（谷歌研究院）
TU Darmstadt（达姆施塔特工业大学）；Mila（米拉人工智能研究所）；Université de Montréal（蒙特利尔大学）；Environment and Climate Change Canada（加拿大环境与气候变化部）；McGill University（麦吉尔大学）
Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To …
Verisk Analytics（韦里斯克分析公司）；Otto-von-Guericke Universität（奥托·冯·格里克大学）；Massachusetts Institute of Technology（麻省理工学院）
Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and intensity are expected to worsen significantly. Unfortunately, general circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately. To address this, we present a multi-resolution deep-learning framework that, firstly, corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales; and secon…
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Public Meteorological Service Center, China Meteorological Administration（中国气象局公共气象服务中心）；University of Bristol（布里斯托大学）；Zhejiang University（浙江大学）；Institute of Atmospheric Physics, Chinese Academy of Science（中国科学院大气物理研究所）；Baidu Inc.（百度公司）；New Jersey Institute of Technology（新泽西理工学院）
As an important symbol of civilization and culture, architectures originally were built for sheltering human beings from weather disasters and therefore should be affected by climate change, particularly the associated change in the occurrence of extreme weather events. However, although meteorology has been considered as a factor in modern architecture design, it remains unclear whether and how the ancients adapted to climate change from the perspective of architecture design, particularly on a millennium time scale. Here we show that the periodic change and trend of the roof slope of ancient…
Rice University（莱斯大学）；University of Pennsylvania（宾夕法尼亚大学）
Convolutional neural networks (CNNs) can potentially provide powerful tools for classifying and identifying patterns in climate and environmental data. However, because of the inherent complexities of such data, which are often spatio-temporal, chaotic, and non-stationary, the CNN algorithms must be designed/evaluated for each specific dataset and application. Yet to start, CNN, a supervised technique, requires a large labeled dataset. Labeling demands (human) expert time, which combined with the limited number of relevant examples in this area, can discourage using CNNs for new problems. To a…
University of California Davis（加州大学戴维斯分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Intel Corporation（英特尔公司）
Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimens…
Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model which provides a stress-test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system; a shallow water approximation for flow over a ridge; and a coarse resolution global atmospheric model with simplified parameterisations (SPEEDY). Although double precision (52 significant bits) is standard across operational climate models, in o…
AI-boosted rare event sampling to characterize extreme weather
LMD/IPSL, CNRS, ENS, Université PSL, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université（巴黎地球物理研究所/法国国家科学研究中心/巴黎高等师范学院/巴黎文理研究大学/巴黎综合理工学院/巴黎理工学院/索邦大学）；Réseau de Transport d’Électricité (RTE)（法国电力电网）；Department of the Geophysical Sciences, University of Chicago（芝加哥大学地球科学系）；Courant Institute, New York University（纽约大学库朗数学科学研究所）
FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Met Office Hadley Centre（英国气象局哈德利中心）
The frequency and magnitude of weather extreme events have increased significantly during the past few years in response to anthropogenic climate change. However, global statistical characteristics and underlying physical mechanisms are still not fully understood. Here, we adopt a statistical physics and probability theory based method to investigate the nature of extreme weather events, particularly the statistics of the day-to-day air temperature differences. These statistical measurements reveal that the distributions of the magnitudes of the extreme events satisfy a universal \textit{Gumbe…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所）；Krea University（克雷亚大学）；University of Petroleum and Energy Studies（石油与能源研究大学）；Queen Mary University of London（伦敦玛丽女王大学）
Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability. Recent developments such as the extraordinary heat waves in California & Canada, and the devastating floods in Germany point to the role of climate change in the ever-increasing frequency of extreme weather. Numerical modelling of the weather and climate have seen tremendous improvements in the last five decades, yet stringent limitations remain to be overcome. Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing…
Predictability horizon properties of chaotic dynamical systems can be related to their spectral properties. It is shown, using this relationship, that the spectral properties of the leading large-scale climate daily indices indicate a smooth predictability: i.e. their intrinsic predictability horizons can be indefinitely extended by reducing the initial error. Special properties of the Madden-Julian Oscillation and some models (including ensemble weather forecasting) have been also discussed in this context.
